Abstract

We present a methodology for extracting the vascular network in the human retina using Dijkstra’s shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online.

© 2012 OSA

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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef]
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2011 (4)

D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011).
[CrossRef]

F. Benmansour and L. Cohen, “Tubular structure segmentation based on minimal path method and anisotropic enhancement,” Int. J. Comput. Vision92, 192–210 (2011).
[CrossRef]

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

R. Estrada, C. Tomasi, M. Cabrera, D. Wallace, S. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2, 2871–2887 (2011).
[CrossRef] [PubMed]

2010 (4)

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18, 19413–19428 (2010).
[CrossRef] [PubMed]

B. Lam, Y. Gao, and A. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag.29, 1369–1381 (2010).
[CrossRef]

G. Lathen, J. Jonasson, and M. Borga, “Blood vessel segmentation using multi-scale quadrature filtering,” Pattern Recogn. Lett.31, 762–767 (2010).
[CrossRef]

A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010).
[CrossRef] [PubMed]

2008 (2)

S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008).
[CrossRef] [PubMed]

D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008).
[CrossRef]

2007 (2)

H. Li and A. Yezzi, “Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines,” IEEE Trans. Med. Imag.26, 1213–1223 (2007).
[CrossRef]

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag.26, 1357–1365 (2007).
[CrossRef]

2006 (2)

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

2004 (4)

O. Wink, W. Niessen, and M. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imag.23, 130–133 (2004).
[CrossRef]

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv.36, 81–121 (2004).
[CrossRef]

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

2002 (2)

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag.19, 203–210 (2002).
[CrossRef]

F. Zana and J. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process.10, 1010–1019 (2002).
[CrossRef]

2000 (1)

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

1989 (1)

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

1960 (1)

J. Cohen, “A Coefficient of agreement for nominal scales,” Educ. Psychol. Meas.20, 37–46 (1960).
[CrossRef]

1959 (1)

E. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math.1, 269–271 (1959).
[CrossRef]

Abramoff, M.

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

Abràmoff, M.

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

Ahmad, S.

S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008).
[CrossRef] [PubMed]

Al-Diri, B.

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

Aquino, A.

D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011).
[CrossRef]

Bellman, R.

R. Bellman, Dynamic Programming (Dover, 2003).

Benmansour, F.

F. Benmansour and L. Cohen, “Tubular structure segmentation based on minimal path method and anisotropic enhancement,” Int. J. Comput. Vision92, 192–210 (2011).
[CrossRef]

Berry, S.

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

Bharath, A.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

Bhattacharya, P.

Q. Li, J. You, L. Zhang, and P. Bhattacharya, “Automated retinal vessel segmentation using Gabor filters and scale multiplication,” in Proceedings of System, Man and Cybernetics (IEEE, 2006), pp. 3521–3527.

Borga, M.

G. Lathen, J. Jonasson, and M. Borga, “Blood vessel segmentation using multi-scale quadrature filtering,” Pattern Recogn. Lett.31, 762–767 (2010).
[CrossRef]

Bravo, J.

D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011).
[CrossRef]

Cabrera, M.

Capone, A.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Cesar, R.

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

Chakraborti, S.

J. Gibbons and S. Chakraborti, Nonparametric Statistical Inference (CRC Press, 2003).

Chanwimaluang, T.

T. Chanwimaluang and G. Fan, “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding,” in Proceedings of the International Symposium on Circuits and Systems (IEEE2003), pp. 21–24.

Chatterjee, S.

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

Chaudhuri, S.

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

Chiang, M. F.

D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008).
[CrossRef]

Chiu, S. J.

Christoffersen, N.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Cohen, J.

J. Cohen, “A Coefficient of agreement for nominal scales,” Educ. Psychol. Meas.20, 37–46 (1960).
[CrossRef]

Cohen, L.

F. Benmansour and L. Cohen, “Tubular structure segmentation based on minimal path method and anisotropic enhancement,” Int. J. Comput. Vision92, 192–210 (2011).
[CrossRef]

Cormen, T.

T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2001).

Cornforth, D.

M. Cree, D. Cornforth, and HF. Jelinek, “Vessel segmentation and tracking using a two-dimensional model,” in Proceedings of Image and Vision Computing New Zealand (IVCNZ, 2005), pp. 345–350.

Cree, M.

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

M. Cree, D. Cornforth, and HF. Jelinek, “Vessel segmentation and tracking using a two-dimensional model,” in Proceedings of Image and Vision Computing New Zealand (IVCNZ, 2005), pp. 345–350.

Dijkstra, E.

E. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math.1, 269–271 (1959).
[CrossRef]

E,

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Ells, A. L.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Ersboll, B.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Estrada, R.

Fan, G.

T. Chanwimaluang and G. Fan, “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding,” in Proceedings of the International Symposium on Circuits and Systems (IEEE2003), pp. 21–24.

Farsiu, S.

Fielder, A. R.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Flynn, J. T.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Freedman, S.

R. Estrada, C. Tomasi, M. Cabrera, D. Wallace, S. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2, 2871–2887 (2011).
[CrossRef] [PubMed]

A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010).
[CrossRef] [PubMed]

S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008).
[CrossRef] [PubMed]

Freedman, S. F.

D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008).
[CrossRef]

Gao, Y.

B. Lam, Y. Gao, and A. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag.29, 1369–1381 (2010).
[CrossRef]

Gegúndez-Arias, M.

D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011).
[CrossRef]

Gibbons, J.

J. Gibbons and S. Chakraborti, Nonparametric Statistical Inference (CRC Press, 2003).

Goldbaum, M.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag.19, 203–210 (2002).
[CrossRef]

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

Gole, G. A.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Good, W. G.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Grunkin, M.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Habib, M.

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

Holmes, J. M.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Holmstrom, G.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Hoover, A.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag.19, 203–210 (2002).
[CrossRef]

Hudaib, T.

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

Hughes, A.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

Hunter, A.

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

Izatt, J. A.

Jelinek, H.

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

Jelinek, HF.

M. Cree, D. Cornforth, and HF. Jelinek, “Vessel segmentation and tracking using a two-dimensional model,” in Proceedings of Image and Vision Computing New Zealand (IVCNZ, 2005), pp. 345–350.

Jonasson, J.

G. Lathen, J. Jonasson, and M. Borga, “Blood vessel segmentation using multi-scale quadrature filtering,” Pattern Recogn. Lett.31, 762–767 (2010).
[CrossRef]

Kaiser, R. S.

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

Katz, N.

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

Katz, X.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Keriven, R.

M. Pechaud, R. Keriven, and G. Peyre, “Extraction of tubular structures over an orientation domain,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2009), pp. 336–342.

Kiely, A.

A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010).
[CrossRef] [PubMed]

Kirbas, C.

C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv.36, 81–121 (2004).
[CrossRef]

Klein, J.

F. Zana and J. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process.10, 1010–1019 (2002).
[CrossRef]

Kouznetsova, V.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag.19, 203–210 (2002).
[CrossRef]

Lam, B.

B. Lam, Y. Gao, and A. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag.29, 1369–1381 (2010).
[CrossRef]

Larsen, M.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Lathen, G.

G. Lathen, J. Jonasson, and M. Borga, “Blood vessel segmentation using multi-scale quadrature filtering,” Pattern Recogn. Lett.31, 762–767 (2010).
[CrossRef]

Leandro, J.

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

Leiserson, C.

T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2001).

Li, H.

H. Li and A. Yezzi, “Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines,” IEEE Trans. Med. Imag.26, 1213–1223 (2007).
[CrossRef]

Li, Q.

Q. Li, J. You, L. Zhang, and P. Bhattacharya, “Automated retinal vessel segmentation using Gabor filters and scale multiplication,” in Proceedings of System, Man and Cybernetics (IEEE, 2006), pp. 3521–3527.

Li, X. T.

Liew, A.

B. Lam, Y. Gao, and A. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag.29, 1369–1381 (2010).
[CrossRef]

Lindeberg, T.

T. Lindeberg, “Edge detection and ridge detection with automatic scale selection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 1996), pp. 465–470.

Loog, M.

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

Madsen, K.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Marín, D.

D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011).
[CrossRef]

Martínez-Pérez, M.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

McNamara, J. A.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

Nelson, M.

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

Nicholas, P.

Niemeijer, M.

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

Niessen, W.

O. Wink, W. Niessen, and M. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imag.23, 130–133 (2004).
[CrossRef]

Palmer, E. A.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Parker, K.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

Patz, A.

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

Pechaud, M.

M. Pechaud, R. Keriven, and G. Peyre, “Extraction of tubular structures over an orientation domain,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2009), pp. 336–342.

Pedersen, L.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Perfetti, R.

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag.26, 1357–1365 (2007).
[CrossRef]

Peyre, G.

M. Pechaud, R. Keriven, and G. Peyre, “Extraction of tubular structures over an orientation domain,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2009), pp. 336–342.

Quek, F.

C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv.36, 81–121 (2004).
[CrossRef]

Quinn, G.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Quinn, G. E.

D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008).
[CrossRef]

Ricci, E.

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag.26, 1357–1365 (2007).
[CrossRef]

Rivest, R.

T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2001).

Sethian, J.

J. Sethian, Level Set Methods and Fast Marching Methods (Cambridge University Press, 1999).

Shapiro, M. J.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Skands, U.

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

Smith, B. T.

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

Soares, J.

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

Staal, J.

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

Stanton, A.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

Steel, D.

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

Stein, C.

T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2001).

Tasman, W.

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

Thom, S.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

Tomasi, C.

Toth, C. A.

Trese, M. G. J.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

Trese, M. T.

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

van Ginneken, B.

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

Viergever, M.

O. Wink, W. Niessen, and M. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imag.23, 130–133 (2004).
[CrossRef]

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

Wallace, D.

R. Estrada, C. Tomasi, M. Cabrera, D. Wallace, S. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2, 2871–2887 (2011).
[CrossRef] [PubMed]

A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010).
[CrossRef] [PubMed]

S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008).
[CrossRef] [PubMed]

Wallace, D. K.

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008).
[CrossRef]

Wink, O.

O. Wink, W. Niessen, and M. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imag.23, 130–133 (2004).
[CrossRef]

Yezzi, A.

H. Li and A. Yezzi, “Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines,” IEEE Trans. Med. Imag.26, 1213–1223 (2007).
[CrossRef]

You, J.

Q. Li, J. You, L. Zhang, and P. Bhattacharya, “Automated retinal vessel segmentation using Gabor filters and scale multiplication,” in Proceedings of System, Man and Cybernetics (IEEE, 2006), pp. 3521–3527.

Zana, F.

F. Zana and J. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process.10, 1010–1019 (2002).
[CrossRef]

Zhang, L.

Q. Li, J. You, L. Zhang, and P. Bhattacharya, “Automated retinal vessel segmentation using Gabor filters and scale multiplication,” in Proceedings of System, Man and Cybernetics (IEEE, 2006), pp. 3521–3527.

Zhao, Z.

A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010).
[CrossRef] [PubMed]

S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008).
[CrossRef] [PubMed]

ACM Comput. Surv. (1)

C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv.36, 81–121 (2004).
[CrossRef]

Am. J. Ophthalmol. (1)

W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006).
[CrossRef] [PubMed]

Arch. Ophthalmol. (2)

G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).

A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010).
[CrossRef] [PubMed]

Biomed. Opt. Express (1)

Educ. Psychol. Meas. (1)

J. Cohen, “A Coefficient of agreement for nominal scales,” Educ. Psychol. Meas.20, 37–46 (1960).
[CrossRef]

IEEE Trans. Image Process. (1)

F. Zana and J. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process.10, 1010–1019 (2002).
[CrossRef]

IEEE Trans. Med. Imag. (9)

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989).
[CrossRef]

H. Li and A. Yezzi, “Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines,” IEEE Trans. Med. Imag.26, 1213–1223 (2007).
[CrossRef]

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag.26, 1357–1365 (2007).
[CrossRef]

J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006).
[CrossRef]

J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004).
[CrossRef]

B. Lam, Y. Gao, and A. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag.29, 1369–1381 (2010).
[CrossRef]

D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011).
[CrossRef]

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag.19, 203–210 (2002).
[CrossRef]

O. Wink, W. Niessen, and M. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imag.23, 130–133 (2004).
[CrossRef]

Int. J. Comput. Vision (1)

F. Benmansour and L. Cohen, “Tubular structure segmentation based on minimal path method and anisotropic enhancement,” Int. J. Comput. Vision92, 192–210 (2011).
[CrossRef]

J. Am. Assoc. Pediatric Ophthalmol. Strabismus (1)

D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008).
[CrossRef]

Numer. Math. (1)

E. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math.1, 269–271 (1959).
[CrossRef]

Opt. Express (1)

Pattern Recogn. Lett. (2)

L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000).
[CrossRef]

G. Lathen, J. Jonasson, and M. Borga, “Blood vessel segmentation using multi-scale quadrature filtering,” Pattern Recogn. Lett.31, 762–767 (2010).
[CrossRef]

Proc. SPIE (1)

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004).
[CrossRef]

Retina (1)

S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008).
[CrossRef] [PubMed]

Other (11)

T. Lindeberg, “Edge detection and ridge detection with automatic scale selection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 1996), pp. 465–470.

J. Sethian, Level Set Methods and Fast Marching Methods (Cambridge University Press, 1999).

R. Bellman, Dynamic Programming (Dover, 2003).

T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2001).

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.

J. Gibbons and S. Chakraborti, Nonparametric Statistical Inference (CRC Press, 2003).

T. Chanwimaluang and G. Fan, “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding,” in Proceedings of the International Symposium on Circuits and Systems (IEEE2003), pp. 21–24.

M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97.
[CrossRef]

M. Cree, D. Cornforth, and HF. Jelinek, “Vessel segmentation and tracking using a two-dimensional model,” in Proceedings of Image and Vision Computing New Zealand (IVCNZ, 2005), pp. 345–350.

Q. Li, J. You, L. Zhang, and P. Bhattacharya, “Automated retinal vessel segmentation using Gabor filters and scale multiplication,” in Proceedings of System, Man and Cybernetics (IEEE, 2006), pp. 3521–3527.

M. Pechaud, R. Keriven, and G. Peyre, “Extraction of tubular structures over an orientation domain,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2009), pp. 336–342.

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Figures (8)

Fig. 1
Fig. 1

Proposed VIO vessel segmentation: In the first stage, VIO images are pre-processed with directional local-contrast filters (DLCF) and LoG-Gabor filters to eliminate artifacts and increase contrast. In the second stage, the best, unvisited vessel pixel in the image is repeatedly chosen as a starting point for a dynamic-programming exploration of the unvisited part of the image. The result of each exploration yields a new tree in the growing forest of vessels. Forest growth stops when the best, unvisited vessel pixel is worse than a predefined threshold.

Fig. 2
Fig. 2

DLCF exudate removal: (a) An image from the STARE dataset [14]. (b) The image after DLCF. (c) Matched filtering [5] applied to (a). (d) Matched filtering applied to (b). The non-vascular filter responses around the exudates have been eliminated in (d) without affecting the true vessel responses.

Fig 3
Fig 3

LoG-Gabor filtering: (a) A sample VIO frame. (b) Frame after LoG-Gabor filtering. (c) A sample mosaic. (d) Mosaic after LoG-Gabor filtering. The isotropic LoG filtering enhances vessel contrast, while the anisotropic Gabor wavelets selectively enhance elongated structures.

Algorithm 1.
Algorithm 1.

Exploratory Dijkstra vessel segmentation: starting from a single pixel, the algorithm progressively explores the rest of the image such that every unvisited pixel has a higher minimum path cost than every visited pixel. The algorithm keeps adding pixels until a cost boundary is reached.

Algorithm 2.
Algorithm 2.

Dijkstra forest vessel segmentation: The algorithm adds disjoint Dijkstra regions until the minimum inverted Laplace-Gabor response at the source pixel exceeds ψ. The operation V \ R represents {xV | xR}.

Fig. 4
Fig. 4

VEVIO images: Two pairs of manually selected frames (a), (c) and two automatically generated mosaics (b), (d). Although each pair was obtained from the same video, the manual frames were not used to generate the mosaics. The mosaics were constructed using selected source frames as described in [29].

Fig. 5
Fig. 5

VEVIO ROI: (a) The original mosaic. (b) The binary mask outlining the ROI for the mosaic in (a). (c) The corresponding manual gold standard. Only pixels that appear white in (b) are taken into account for the metrics tallied in our results.

Fig. 6
Fig. 6

Vessel segmentation on a mosaic: (a) Original image (b) Manual segmentation (c) Dijkstra forest (d) Matched filters (e) Local entropy (f) GMM classifier (g) KNN classifier

Tables (4)

Tables Icon

Table 1 Segmentation results on the test set *

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Table 2 Segmentation results on the single (not mosaiced) test frames *

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Table 3 Segmentation results on the test mosaics *

Tables Icon

Table 4 Parameter values

Equations (12)

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max ( | x x | , | y y | ) = 1 .
c ( e ) = m = 1 4 w m e α z m ( e ) where m = 1 4 w m = 1
z ( e ) = z ( v , v ) = [ I g ( v ) , | I g ( v ) I g ( v ) | , F ( v ) , | F ( v ) F ( v ) | ] .
γ ( v , v ) = ( v = v 1 , v 2 , , v k = v ) ,
c ( γ ) = i = 1 k 1 c ( v i , v i + 1 ) .
c ( γ ( v , v ) ) = c ( γ ( v , v i ) ) + c ( γ ( v i , v ) ) for any i [ 2 , k 1 ] ,
γ ˜ ( v , v ) = argmin c ( γ ) γ Γ ( v , v ) ,
R τ ( s ) = { v | γ ˜ ( s , v ) τ } .
R = { R 0 , R 1 , , R K } , where F ( s 0 ) F ( s K ) ψ .
accuracy = t p + t n t p + t n + f p + f n
F 1 = 2 precision recall precision + recall
precision = t p t p + f p , recall = t p t p + f n .

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